Learning control policy for parcel singulation

Parcel singulation is one of the key technology in the automated logistics process. In order to manipulate parcels independently, we propose to use sparse actuator array, which has advantages of cost reduction while the control of singulation is difficult because of actuator sharing. In this paper, intuitive control is introduced to make a bundle of parcels lined in order of priority, whose assignment function is learned from massive simulated episodes. Numerical simulation validates the proposed algorithm and the results show that the learning improves the singulation performance.